Multi-band transmission technologies promise to cost-effectively expand the capacity of optical networks by exploiting low-loss spectrum windows beyond the conventional band used in already-deployed fibers. While such technologies offer a high potential for capacity upgrades, available capacity is seriously restricted not only by the wavelength-continuity constraint but also by the signal-to-noise ratio (SNR) constraint. In fact, exploiting more bands can cause higher SNR imbalance over multiple bands, which is mainly due to stimulated Raman scattering. To relax these constraints, we propose wavelength-selective band switching-enabled networks (BSNs), where each wavelength channel can be freely switched to any band and in any direction at any optical node on the route. We also present two typical optical node configurations utilizing all-optical wavelength converters, which can realize the switching proposal. Moreover, numerical analyses clarify that our BSN can reduce the fiber resource requirements by more than 20% compared to a conventional multi-band network under realistic conditions. We also discuss the impact of physical-layer performance of band switching operations on available benefits to investigate the feasibility of BSNs. In addition, we report on a proof-of-concept demonstration of a BSN with a prototype node, where C+L-band wavelength-division-multiplexed 112-Gb/s dual-polarization quadrature phase-shift keying signals are successfully transmitted while the bands of individual channels are switched node-by-node for up to 4 cascaded nodes.

With the network function virtualization technology, a middlebox can be deployed as software on commercial servers rather than on dedicated physical servers. A backup server is necessary to ensure the normal operation of the middlebox. The workload can affect the failure rate of backup server; the impact of workload-dependent failure rate on backup server allocation considering unavailability has not been extensively studied. This paper proposes a shared backup allocation model of middlebox with consideration of the workload-dependent failure rate of backup server. Backup resources on a backup server can be assigned to multiple functions. We observe that a function has four possible states and analyze the state transitions within the system. Through the queuing approach, we compute the probability of each function being available or unavailable for a certain assignment, and obtain the unavailability of each function. The proposed model is designed to find an assignment that minimizes the maximum unavailability among functions. We develop a simulated annealing algorithm to solve this problem. We evaluate and compare the performances of proposed and baseline models under different experimental conditions. Based on the results, we observe that, compared to the baseline model, the proposed model reduces the maximum unavailability by an average of 29% in our examined cases.

Mobile edge computing (MEC) technology guarantees the privacy and security of large-scale data in the Narrowband-IoT (NB-IoT) by deploying MEC servers near base stations to provide sufficient computing, storage, and data processing capacity to meet the delay and energy consumption requirements of NB-IoT terminal equipment. For the NB-IoT MEC system, this paper proposes a resource allocation algorithm based on deep reinforcement learning to optimize the total cost of task offloading and execution. Since the formulated problem is a mixed-integer non-linear programming (MINLP), we cast our problem as a multi-agent distributed deep reinforcement learning (DRL) problem and address it using dueling Q-learning network algorithm. Simulation results show that compared with the deep Q-learning network and the all-local cost and all-offload cost algorithms, the proposed algorithm can effectively guarantee the success rates of task offloading and execution. In addition, when the execution task volume is 200KBit, the total system cost of the proposed algorithm can be reduced by at least 1.3%, and when the execution task volume is 600KBit, the total cost of system execution tasks can be reduced by 16.7% at most.

This paper proposes a scheme that reduces residual self-interference significantly in the analog-circuit domain on wireless full-duplex relay systems. Full-duplex relay systems utilize the same time and frequency resources for transmission and reception at the relay node to improve spectral efficiency. Our proposed scheme measures multiple responses of the feedback path by changing the direction of the main beam of the transmitter at the relay, and then selecting the optimal direction that minimizes the residual self-interference. Analytical residual self-interference is derived as the criterion to select the optimal direction. In addition, this paper considers the target of residual self-interference power before the analog-to-digital converter (ADC) dependent on the dynamic range in the analog-circuit domain. Analytical probability that the residual interference exceeds the target is derived to help in determining the number of measured responses of the feedback path. Computer simulations validate the analytical results, and show that in particular, the proposed scheme with ten candidates improves the residual self-interference by approximately 6dB at the probability of 0.01 that the residual self-interference exceeds target power compared with a conventional scheme with the feedback path modeled as Rayleigh fading.

In this paper, a metadata-based quality-estimation model is proposed for tile-based omnidirectional video streaming services, aiming to realize quality monitoring during service provision. In the tile-based omnidirectional video (ODV) streaming services, the ODV is divided into tiles, and the high-quality tiles and the low-quality tiles are distributed in accordance with the user's viewing direction. When the user changes the viewing direction, the user temporarily watches video with the low-quality tiles. In addition, the longer the time (delay time) until the high-quality tile for the new viewing direction is downloaded, the longer the viewing time of video with the low-quality tile, and thus the delay time affects quality. From the above, the video quality of the low-quality tiles and the delay time significantly impact quality, and these factors need to be considered in the quality-estimation model. We develop quality-estimation models by extending the conventional quality-estimation models for 2D adaptive streaming. We also show that the quality-estimation model using the bitrate, resolution, and frame rate of high- and low-quality tiles and that the delay time has sufficient estimation accuracy based on the results of subjective quality evaluation experiments.

We have designed a near-infrared wavelength Si/CaF2 DFB quantum cascade laser and investigated the possibility of single-mode laser oscillation by analysis of the propagation mode, gain, scattering time of Si quantum well, and threshold current density. As the waveguide and resonator, a slab-type waveguide structure with a Si/CaF2 active layer sandwiched by SiO2 on a Si (111) substrate and a grating structure in an n-Si conducting layer were assumed. From the results of optical propagation mode analysis, by assuming a λ/4-shifted bragg waveguide structure, it was found that the single vertical and horizontal TM mode propagation is possible at the designed wavelength of 1.70µm. In addition, a design of the active layer is proposed and its current injection capability is roughly estimated to be 25.1kA/cm2, which is larger than required threshold current density of 1.4kA/cm2 calculated by combining analysis results of the scattering time, population inversion, gain of quantum cascade lasers, and coupling theory of a Bragg waveguide. The results strongly indicate the possibility of single-mode laser oscillation.

Parallel line feeder (PLF) consisting of a two-wire transmission line operating in the MHz band has been proposed as a wide-coverage short-distance wireless charging. In the MHz band, a PLF of several meters suffers from standing wave effect, resulting in fluctuation in power transfer efficiency accordingly to the receiver's position. This paper studies a modified version of the system, where the PLF is divided into individually compensated segments to mitigate the standing wave effect. Modelling the PLF as a lossy transmission line, this paper theoretically shows that if the segments' lengths are properly determined, it is able to improve and stabilize the efficiency for all positions. Experimental results at 27.12 MHz confirm the theoretical analysis and show that a fairly high efficiency of 70% can be achieved.

We achieved detailed characterization of resonant tunneling chaos generator circuits in microwave frequency range. The circuit is analogous to Duffing oscillator, where the third-order nonlinear potential term is emulated by the nonlinear current-voltage curve of the resonant tunneling diode. The circuit includes a periodic reset mechanism to output identical chaos signal, which is essential to observe chaos signal on a sampling oscilloscope. Though this was shown to be effective in our previous papers, the length of the waveforms to observe is limited to rather short period, and it was unclear if this technique can be used for detailed characterization of such high-frequency chaos. In this paper, we improved the circuit design to observe longer waveforms, and demonstrated that the detailed characterization is possible using this periodic resetting technique with a sampling oscilloscope. The hybrid integration scheme is also used in this paper, which allows the easiest and shortest way to mimic a circuit as per circuit design, and precise estimation of circuit parameters aiming to eliminate circuit-related abnormalities. We provide deep insight into the dynamics associated with our circuit, starting from the single period, double period, chaos, and triple period regimes, by extracting power spectra, return maps, phase portraits, and bifurcation diagrams from acquired time series using sampling oscilloscope. Our method to study microwave chaotic signals can be applied to much higher frequency ranges, such as THz frequency range.

In this paper, a design method for an over octave hybrid continuous mode power amplifier (PA) based on modified real frequency technique (MRFT) is proposed. The extended continuous class-F/F-1 modes greatly expand the design space, which provides the possibility of over octave design, the optimal impedances at internal current-generator (I-Gen) plane and package plane are investigated. Then a novel broadband matching network based on MRFT is presented for impedance match. To verify the proposed methodology, an over octave PA with radial stub is fabricated and measured. The PA achieves a bandwidth of 133% from 0.8GHz to 4GHz, over this frequency range, the drain efficiency is 58.3-68.7% and large-signal gain is greater than 9.6dB.

Visual Question Answering (VQA) usually uses deep attention mechanisms to learn fine-grained visual content of images and textual content of questions. However, the deep attention mechanism can only learn high-level semantic information while ignoring the impact of the low-level semantic information on answer prediction. For such, we design a High- and Low-Level Semantic Information Network (HLSIN), which employs two strategies to achieve the fusion of high-level semantic information and low-level semantic information. Adaptive weight learning is taken as the first strategy to allow different levels of semantic information to learn weights separately. The gate-sum mechanism is used as the second to suppress invalid information in various levels of information and fuse valid information. On the benchmark VQA-v2 dataset, we quantitatively and qualitatively evaluate HLSIN and conduct extensive ablation studies to explore the reasons behind HLSIN's effectiveness. Experimental results demonstrate that HLSIN significantly outperforms the previous state-of-the-art, with an overall accuracy of 70.93% on test-dev.

Fine-grained image classification is one of the key basic tasks of computer vision. The appearance of traditional deep convolutional neural network (DCNN) combined with attention mechanism can focus on partial and local features of fine-grained images, but it still lacks the consideration of the embedding mode of different attention modules in the network, leading to the unsatisfactory result of classification model. To solve the above problems, three different attention mechanisms are introduced into the DCNN network (like ResNet, VGGNet, etc.), including SE, CBAM and ECA modules, so that DCNN could better focus on the key local features of salient regions in the image. At the same time, we adopt three different embedding modes of attention modules, including serial, residual and parallel modes, to further improve the performance of the classification model. The experimental results show that the three attention modules combined with three different embedding modes can improve the performance of DCNN network effectively. Moreover, compared with SE and ECA, CBAM has stronger feature extraction capability. Among them, the parallelly embedded CBAM can make the local information paid attention to by DCNN richer and more accurate, and bring the optimal effect for DCNN, which is 1.98% and 1.57% higher than that of original VGG16 and Resnet34 in CUB-200-2011 dataset, respectively. The visualization analysis also indicates that the attention modules can be easily embedded into DCNN networks, especially in the parallel mode, with stronger generality and universality.

As a further investigation of the image captioning task, some works extended the vision-text dataset for specific subtasks, such as the stylized caption generating. The corpus in such dataset is usually composed of obvious sentiment-bearing words. While, in some special cases, the captions are classified depending on image category. This will result in a latent problem: the generated sentences are in close semantic meaning but belong to different or even opposite categories. It is a worthy issue to explore an effective way to utilize the image category label to boost the caption difference. Therefore, we proposed an image captioning network with the label control mechanism (LCNET) in this paper. First, to further improve the caption difference, LCNET employs a semantic enhancement module to provide the decoder with global semantic vectors. Then, through the proposed label control LSTM, LCNET can dynamically modulate the caption generation depending on the image category labels. Finally, the decoder integrates the spatial image features with global semantic vectors to output the caption. Using all the standard evaluation metrics shows that our model outperforms the compared models. Caption analysis demonstrates our approach can improve the performance of semantic representation. Compared with other label control mechanisms, our model is capable of boosting the caption difference according to the labels and keeping a better consistent with image content as well.

The original single-shot multibox detector (SSD) algorithm has good detection accuracy and speed for regular object recognition. However, the SSD is not suitable for detecting small objects for two reasons: 1) the relationships among different feature layers with various scales are not considered, 2) the predicted results are solely determined by several independent feature layers. To enhance its detection capability for small objects, this study proposes an improved SSD-based algorithm called proportional channels' fusion SSD (PCF-SSD). Three enhancements are provided by this novel PCF-SSD algorithm. First, a fusion feature pyramid model is proposed by concatenating channels of certain key feature layers in a given proportion for object detection. Second, the default box sizes are adjusted properly for small object detection. Third, an improved loss function is suggested to train the above-proposed fusion model, which can further improve object detection performance. A series of experiments are conducted on the public database Pascal VOC to validate the PCF-SSD. On comparing with the original SSD algorithm, our algorithm improves the mean average precision and detection accuracy for small objects by 3.3% and 3.9%, respectively, with a detection speed of 40FPS. Furthermore, the proposed PCF-SSD can achieve a better balance of detection accuracy and efficiency than the original SSD algorithm, as demonstrated by a series of experimental results.

Ontologies are regarded as the solution to data heterogeneity on the Semantic Web (SW), but they also suffer from the heterogeneity problem, which leads to the ambiguity of data information. Ontology Meta-Matching technique (OMM) is able to solve the ontology heterogeneity problem through aggregating various similarity measures to find the heterogeneous entities. Inspired by the success of Reinforcement Learning (RL) in solving complex optimization problems, this work proposes a RL-based OMM technique to address the ontology heterogeneity problem. First, we propose a novel RL-based OMM framework, and then, a neural network that is called evaluated network is proposed to replace the Q table when we choose the next action of the agent, which is able to reduce memory consumption and computing time. After that, to better guide the training of neural network and improve the accuracy of RL agent, we establish a memory bank to mine depth information during the evaluated network's training procedure, and we use another neural network that is called target network to save the historical parameters. The experiment uses the famous benchmark in ontology matching domain to test our approach's performance, and the comparisons among Deep Reinforcement Learning(DRL), RL and state-of-the-art ontology matching systems show that our approach is able to effectively determine high-quality alignments.

Tool condition monitoring is one of the core tasks of intelligent manufacturing in digital workshop. This paper presents an intelligent recognize method of tool condition based on deep learning. First, the industrial microphone is used to collect the acoustic signal during machining; then, a central fractal decomposition algorithm is proposed to extract sensitive information; finally, the multi-scale convolutional recurrent neural network is used for deep feature extraction and pattern recognition. The multi-process milling experiments proved that the proposed method is superior to the existing methods, and the recognition accuracy reached 88%.

Automatic continuous tracking of objects involved in a construction project is required for such tasks as productivity assessment, unsafe behavior recognition, and progress monitoring. Many computer-vision-based tracking approaches have been investigated and successfully tested on construction sites; however, their practical applications are hindered by the tracking accuracy limited by the dynamic, complex nature of construction sites (i.e. clutter with background, occlusion, varying scale and pose). To achieve better tracking performance, a novel deep-learning-based tracking approach called the Multi-Domain Convolutional Neural Networks (MD-CNN) is proposed and investigated. The proposed approach consists of two key stages: 1) multi-domain representation of learning; and 2) online visual tracking. To evaluate the effectiveness and feasibility of this approach, it is applied to a metro project in Wuhan China, and the results demonstrate good tracking performance in construction scenarios with complex background. The average distance error and F-measure for the MDNet are 7.64 pixels and 67, respectively. The results demonstrate that the proposed approach can be used by site managers to monitor and track workers for hazard prevention in construction sites.

The traditional manual inspection is gradually replaced by the unmanned aerial vehicles (UAV) automatic inspection. However, due to the limited computational resources carried by the UAV, the existing deep learning-based algorithm needs a large amount of computational resources, which makes it impossible to realize the online detection. Moreover, there is no effective online detection system at present. To realize the high-precision online detection of electrical equipment, this paper proposes an SSD (Single Shot Multibox Detector) detection algorithm based on the improved Dual network for the images of insulators and spacers taken by UAVs. The proposed algorithm uses MnasNet and MobileNetv3 to form the Dual network to extract multi-level features, which overcomes the shortcoming of single convolutional network-based backbone for feature extraction. Then the features extracted from the two networks are fused together to obtain the features with high-level semantic information. Finally, the proposed algorithm is tested on the public dataset of the insulator and spacer. The experimental results show that the proposed algorithm can detect insulators and spacers efficiently. Compared with other methods, the proposed algorithm has the advantages of smaller model size and higher accuracy. The object detection accuracy of the proposed method is up to 95.1%.

Recent studies in protein structure prediction such as AlphaFold have enabled deep learning to achieve great attention on the Drug-Target Affinity (DTA) task. Most works are dedicated to embed single molecular property and homogeneous information, ignoring the diverse heterogeneous information gains that are contained in the molecules and interactions. Motivated by this, we propose an end-to-end deep learning framework to perform Molecular Heterogeneous features Fusion (MolHF) for DTA prediction on heterogeneity. To address the challenges that biochemical attributes locates in different heterogeneous spaces, we design a Molecular Heterogeneous Information Learning module with multi-strategy learning. Especially, Molecular Heterogeneous Attention Fusion module is present to obtain the gains of molecular heterogeneous features. With these, the diversity of molecular structure information for drugs can be extracted. Extensive experiments on two benchmark datasets show that our method outperforms the baselines in all four metrics. Ablation studies validate the effect of attentive fusion and multi-group of drug heterogeneous features. Visual presentations demonstrate the impact of protein embedding level and the model ability of fitting data. In summary, the diverse gains brought by heterogeneous information contribute to drug-target affinity prediction.

The data augmentation method is known as a helpful technique to generate a dataset with a large number of images from one with a small number of images for supervised training in deep learning. However, a low validity augmentation method for image recognition was reported in a recent study on artificial intelligence (AI). This study aimed to clarify the optimal data augmentation method in deep learning model generation for the recognition of white blood cells (WBCs). Study Design: We conducted three different data augmentation methods (rotation, scaling, and distortion) on original WBC images, with each AI model for WBC recognition generated by supervised training. The subjects of the clinical assessment were 51 healthy persons. Thin-layer blood smears were prepared from peripheral blood and subjected to May-Grünwald-Giemsa staining. Results: The only significantly effective technique among the AI models for WBC recognition was data augmentation with rotation. By contrast, the effectiveness of both image distortion and image scaling was poor, and improved accuracy was limited to a specific WBC subcategory. Conclusion: Although data augmentation methods are often used for achieving high accuracy in AI generation with supervised training, we consider that it is necessary to select the optimal data augmentation method for medical AI generation based on the characteristics of medical images.

In the pork fat content detection task, traditional physical or chemical methods are strongly destructive, have substantial technical requirements and cannot achieve nondestructive detection without slaughtering. To solve these problems, we propose a novel, convenient and economical method for detecting the fat content of pig B-ultrasound images based on hybrid attention and multiscale fusion learning, which extracts and fuses shallow detail information and deep semantic information at multiple scales. First, a deep learning network is constructed to learn the salient features of fat images through a hybrid attention mechanism. Then, the information describing pork fat is extracted at multiple scales, and the detailed information expressed in the shallow layer and the semantic information expressed in the deep layer are fused later. Finally, a deep convolution network is used to predict the fat content compared with the real label. The experimental results show that the determination coefficient is greater than 0.95 on the 130 groups of pork B-ultrasound image data sets, which is 2.90, 6.10 and 5.13 percentage points higher than that of VGGNet, ResNet and DenseNet, respectively. It indicats that the model could effectively identify the B-ultrasound image of pigs and predict the fat content with high accuracy.